In recent years, deep learning approaches [1]}, [2]}, [3]}, [4]} have demonstrated superior performance for photon-efficient imaging in high-noise scenarios. Lindell et al. [1]} proposed a network based on U-Net [6]} for imaging at low signal-to-background ratios (SBRs) with sensor fusion strategy. However, this approach requires additional intensity images which are difficult to obtain in realistic scenarios. Peng et al. [2]} proposed a non-local neural network to extract the long-range correlations among the photon counting data across the whole temporal dimension. However, the large computational and memory consumption of the non-local block may reduce the efficiency of this model. Based on U-Net++ [8]}, Zang et al. [3]} built a network with rich skip connections between multi-scale features to capture the spatial patterns, while such complex connections are more compatible with clean data. Generally speaking, existing deep learning models focus more on capturing spatial or temporal correlations from the data while lack a careful consideration for denoising. As a result, the robustness against noise cannot be guaranteed, i.e., these algorithms may fail to behave stably at different SBRs. For this reason, the goal of this paper is to design a denoising network to achieve robust imaging performance at different SBRs.
